نتایج جستجو برای: fast independent component analysis fastica
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Independent Component Analysis (ICA) can be described in several ways, one of which is as a technique that seeks to find a set directions (components) underlying multivariate data that are most independent of one another. While there are several ICA models and many ICA methods, in this report we focus on the most basic model and one of the most popular and simple algorithms; the One-Unit FastIC...
Independent component analysis (ICA) is possibly the most widespread approach to solve the blind source separation problem. Many different algorithms have been proposed, together with several highly successful applications. There is also an extensive body of work on the theoretical foundations and limits of the ICA methodology. One practical concern about the use of ICA with real world data is ...
Geometric algorithms for linear independent component analysis (ICA) have recently received some attention due to their pictorial description and their relative ease of implementation. The geometric approach to ICA was proposed first by Puntonet and Prieto (1995). We will reconsider geometric ICA in a theoretic framework showing that fixed points of geometric ICA fulfill a geometric convergence...
Service quality and efficiency of urban systems have been dramatically boosted by various high technologies for real-time monitoring remote control, also gained privileged space in water distribution. Monitored hydraulic parameters are crucial data developing planning, operation security analyses networks, which makes them increasingly reliable. However, devices control increase the possibiliti...
In this paper, we utilize speech signal to design sound-activated designation for confining an automaton to specific physical space. This work will present the design and analysis of multi-source extraction and localization to application of ubiquitous sound activation system. To extract a speaker sound from interference sources (such as a babble noise generated from TV playing), a multi-source...
This paper deals with blind speech separation of convolutive mixtures of sources. The separation criterion is based on Oriented Principal Components Analysis (OPCA) in the frequency domain. OPCA is a (second order) extension of standard Principal Component Analysis (PCA) aiming at maximizing the power ratio of a pair of signals. The convolutive mixing is obtained by modeling the Head Related Tr...
We examine differences between independent component analyses (ICAs) arising from different assumptions, measures of dependence, and starting points of the algorithms. ICA is a popular method with diverse applications including artifact removal in electrophysiology data, feature extraction in microarray data, and identifying brain networks in functional magnetic resonance imaging (fMRI). ICA ca...
Group-ICA [Smith 2014a, FastICA/MELODIC] was applied at 5 dimensionalities (d=25,50,100,200,300) to preprocessed rfMRI data, with surfacebased alignment (“MSM-sulc”) utilising folding patterns to align different subjects’ surfaces with each other [Glasser 2013, Salimi-Khorshidi 2014, Robinson 2014]. These group-ICA “parcellations” (where each ICA component comprises a “node” or “parcel”) were u...
We present a method for evaluating ICA separation of artifacts from EEG (electroencephalographic) data. Two algorithms, Infomax and FastICA, were applied to "synthetic data," created by superimposing simulated blinks on a blink-free EEG. To examine sensitivity to different data characteristics, multiple datasets were constructed by varying properties of the simulated blinks. ICA was used to dec...
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